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New Q-Learning Algorithms Offer Fine-Grained Regret Bounds

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new algorithms for Q-learning that provide more precise regret bounds in episodic tabular Markov Decision Processes. These advancements address limitations in existing methods by offering fine-grained, gap-dependent regret guarantees. The study introduces a novel analytical framework and proposes new algorithms, ULCB-Hoeffding and a refined AMB, which demonstrate improved performance and theoretical rigor. AI

RANK_REASON The cluster contains an academic paper detailing new algorithms and theoretical findings in reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Q-Learning Algorithms Offer Fine-Grained Regret Bounds

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The cluster contains an academic paper detailing new algorithms and theoretical findings in reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Haochen Zhang, Zhong Zheng, Lingzhou Xue ·

    Q-Learning with Fine-Grained Gap-Dependent Regret

    arXiv:2510.06647v2 Announce Type: replace-cross Abstract: We study fine-grained gap-dependent regret bounds for model-free reinforcement learning in episodic tabular Markov Decision Processes. Existing model-free algorithms achieve minimax worst-case regret, but their gap-depende…

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